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Hemodialysis (HD) is a treatment given to patients with renal failure. Notable treatment-related complications include hypotension, cramps, insufficient blood flow, and arrhythmia. Most complications are associated with unstable blood pressure during HD. Physicians are devoted to seeking solutions to prevent or lower the incidence of possible complications. With advances in technology, big data have been obtained in various medical fields. The accumulated dialysis records in each HD session can be gathered to obtain big HD data with the potential to assist HD staff in increasing patient wellbeing. We generated a large stream of HD parameters collected from dialysis equipment associated with the Vital Info Portal gateway and correlated with the demographic data stored in the hospital information system from each HD session. We expect that the application of HD big data will greatly assist HD staff in treating intradialytic hypotension, setting optimal dialysate parameters, and even developing an intelligent early-warning system as well as providing individualized suggestions regarding dialysis settings in the future.


Cheng-Jui Lin, Ying-Ying Chen, Chi-Feng Pan, Vincent Wu, Chih-Jen Wu. Dataset supporting blood pressure prediction for the management of chronic hemodialysis. Scientific data. 2019 Dec 09;6(1):313

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PMID: 31819065

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